ABSTRACT Anxiety disorders are the most common mental illness in the United States. Currently the most effective therapies for anxiety disorders include behavioral treatments which are predicated on laboratory methods of human fear conditioning (FC) and fear extinction (FE). However, treatment efficacy varies widely across individuals and relapse rates can be high. This may reflect the artificiality of the FC and FE paradigms on which these treatments are based, specifically how they do not incorporate different forms of uncertainty inherent in most real-world situations. As it stands, there is currently no understanding of how different kinds of uncertainty affect brain activity during fear learning nor how these effects can alter the subsequent extinction of those fears. In fact, the FC literature implies that all forms of uncertainty will engage the same neural processes and result in the same behavioral outcomes. As a result, certain forms of uncertainty that are present in all real- world situations such as temporal uncertainty (TU) have never been studied in the absence of confounds. We have developed a novel method that allows us to study the effect of un-confounded TU on fear learning. Preliminary behavioral data collected via this method indicates that TU is significantly more arousing that predictability and subsequent computer simulations support the feasibility of using this method for measuring the effects of TU in a neuroimaging environment. Guided by these findings, we propose to use this method to achieve three specific aims meant to identify how different forms of uncertainty affect FC and FE. In Specific Aim 1 we will establish the behavioral, physiological, and neural responses to experiencing TU during fear learning, as this form of uncertainty has not yet been studied in the absence of confounds. In Specific Aim 2 we will contrast the neural effects of TU to those of reinforcement uncertainty (RU) to investigate if different forms of uncertainty are processed by unique brain areas or if they are processed by a common ?uncertainty module.? Finally, in Specific Aim 3, we will analyze how exposure to each of these forms of uncertainty affects standard FE where no uncertainty is incorporated into the paradigm. Collectively, this research will represent a vast departure from currently accepted FC models which do not acknowledge different kinds of uncertainty as capable of having unique effects on learning. Further, this work will demonstrate the efficacy of our novel paradigm at measuring un-confounded TU thus establishing it as a useful metric that can be used in future FC studies. Finally, our results will demonstrate the necessity of incorporating different kinds of uncertainty into FC and FE paradigms in order to create more realistic and individualized treatments for anxiety.